Multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem
Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi-objective evolutionary algorithm dealing with this problem update current population without any guidance from previous se...
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| Vydáno v: | Information sciences Ročník 609; s. 387 - 410 |
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01.09.2022
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| Abstract | Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi-objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi-objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function network (RBFN) is exploited to learn the potential knowledge of individuals, generate hypothesis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into account the non-dominated relationship between individuals. Moreover, integrated with a specific non-dominated sorting strategy, i.e., ENS-SS, along with several effective heuristic operations, the proposed algorithm performs favorably for solving the MO-VRPSD. The experimental results based on the modified Solomon benchmark instances verified the effectiveness of the respective components, and the superiority to other multi-objective evolutionary algorithms. |
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| AbstractList | Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi-objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi-objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function network (RBFN) is exploited to learn the potential knowledge of individuals, generate hypothesis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into account the non-dominated relationship between individuals. Moreover, integrated with a specific non-dominated sorting strategy, i.e., ENS-SS, along with several effective heuristic operations, the proposed algorithm performs favorably for solving the MO-VRPSD. The experimental results based on the modified Solomon benchmark instances verified the effectiveness of the respective components, and the superiority to other multi-objective evolutionary algorithms. |
| Author | Song, Wen Shao, Jie Cao, Zhiguang Niu, Yunyun Xiao, Jianhua |
| Author_xml | – sequence: 1 givenname: Yunyun surname: Niu fullname: Niu, Yunyun organization: School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China – sequence: 2 givenname: Jie surname: Shao fullname: Shao, Jie organization: School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China – sequence: 3 givenname: Jianhua surname: Xiao fullname: Xiao, Jianhua email: jhxiao@nankai.edu.cn organization: Research Center of Logistics, Nankai University, Tianjin 300071, China – sequence: 4 givenname: Wen surname: Song fullname: Song, Wen organization: Institute of Marine Science and Technology, Shandong University, Shandong 250100, China – sequence: 5 givenname: Zhiguang surname: Cao fullname: Cao, Zhiguang organization: Singapore Institute of Manufacturing Technology, Singapore 138634, Singapore |
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| Cites_doi | 10.3390/info9070167 10.1109/4235.996017 10.1007/s00500-019-04312-9 10.1287/opre.40.3.574 10.1023/A:1007677805582 10.1023/A:1022643204877 10.1142/S0219649214500221 10.1007/s10479-015-1949-7 10.1080/00401706.1971.10488811 10.1287/opre.35.2.254 10.1016/j.knosys.2021.107378 10.1007/978-3-319-93025-1_4 10.1109/TEVC.2014.2308305 10.1016/S0952-1976(02)00011-8 10.1016/j.neucom.2011.03.027 10.1016/j.procs.2014.09.077 10.1109/5.58326 10.1016/S0888-3270(03)00080-3 10.1016/j.ejor.2005.12.029 10.1007/s10710-005-6164-x |
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| Keywords | Learnable evolution model Radial basis function network Multi-objective evolutionary algorithm Vehicle routing problem Stochastic demand |
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| References | Poggio, Girosi (b0145) 1990; 78 Van Veldhuizen, Lamont (b0210) 1998 Christofides, Mingozzi, Toth (b0040) 1979 Wojtusiak (b0215) 2012; 28 A. Hassanat, V. Prasath, M. Abbadi, S. Abu-Qdari, H. Faris, An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques, Information 9 (2018). URL D. Bertsimas, A Vehicle Routing Problem with Stochastic Demand, Oper. Res. 40 (1992) 574–585. Herrera, Pomares, Rojas, Guillén, Rubio, Urquiza (b0075) 2011; 74 Gee, Arokiasami, Jiang, Tan (b0070) 2016 Allen (b0015) 1971; 13 K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002) 182–197. K. Tan, L. Lee, K. Ou, Artificial intelligence heuristics in solving vehicle routing problems with time window constraints, Eng. Appl. Artif. Intell. 14 (2001) 825–837. Riquelme, Von Lücken, Baran (b0130) 2015 S. Mirjalili, Genetic algorithm, Evol. Algorithms Neural Networks (2019) 43–55. Solomon (b0175) 1987; 35 Sarasola, Doerner, Schmid, Alba (b0190) 2016; 236 Fasshauer (b0065) 2007 Moradi (b0100) 2020 Wuxing, Peter, Guicai, Tielin (b0225) 2004; 18 Tan, Cheong, Goh (b0205) 2007; 177 Broomhead, Lowe (b0025) 1988 F. Schwenker, H. Kestler, G. Palm, Contributed article Three (2000). E. Berhan, B. Beshah, D. Kitaw, A. Abraham, Stochastic Vehicle Routing Problem: A Literature Survey, J. Inf. Knowl. Manage. 13 (2014). J. Moody, C. Darken, Learning with localized receptive fields, Yale Univ., Department of Computer Science, 1988. Y. Niu, D. Kong, R. Wen, Z. Cao, J. Xiao, An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand, Knowl.-Based Syst. 230 (2021) 107378. C. Liong, I. Wan, O. Khairuddin, Vehicle Routing Problem: Models and Solutions, J. Quality Meas. Anal. (2008). Curry, Dagli (b0055) 2014; 36 Coello, Cortés (b0045) 2005; 6 Michalski (b0105) 2000; 38 . Lawrence, Tsoi, Back (b0095) 1996; 1621 Schott (b0195) 1995 Quinlan (b0150) 1986; 1 X. Zhang, Y. Tian, R. Cheng, Y. Jin, An Efficient Approach to Non-dominated Sorting for Evolutionary Multi-objective Optimization, IEEE Trans. Evol. Comput. 19 (2015) 201–213. 10.1016/j.ins.2022.07.087_b0080 Christofides (10.1016/j.ins.2022.07.087_b0040) 1979 Quinlan (10.1016/j.ins.2022.07.087_b0150) 1986; 1 Coello (10.1016/j.ins.2022.07.087_b0045) 2005; 6 10.1016/j.ins.2022.07.087_b0185 Herrera (10.1016/j.ins.2022.07.087_b0075) 2011; 74 10.1016/j.ins.2022.07.087_b0120 10.1016/j.ins.2022.07.087_b0200 10.1016/j.ins.2022.07.087_b0125 Riquelme (10.1016/j.ins.2022.07.087_b0130) 2015 Schott (10.1016/j.ins.2022.07.087_b0195) 1995 10.1016/j.ins.2022.07.087_b0245 Curry (10.1016/j.ins.2022.07.087_b0055) 2014; 36 Lawrence (10.1016/j.ins.2022.07.087_b0095) 1996; 1621 Allen (10.1016/j.ins.2022.07.087_b0015) 1971; 13 Sarasola (10.1016/j.ins.2022.07.087_b0190) 2016; 236 Broomhead (10.1016/j.ins.2022.07.087_b0025) 1988 Michalski (10.1016/j.ins.2022.07.087_b0105) 2000; 38 Poggio (10.1016/j.ins.2022.07.087_b0145) 1990; 78 10.1016/j.ins.2022.07.087_b0090 Fasshauer (10.1016/j.ins.2022.07.087_b0065) 2007 10.1016/j.ins.2022.07.087_b0050 10.1016/j.ins.2022.07.087_b0030 10.1016/j.ins.2022.07.087_b0035 Moradi (10.1016/j.ins.2022.07.087_b0100) 2020 10.1016/j.ins.2022.07.087_b0135 Wojtusiak (10.1016/j.ins.2022.07.087_b0215) 2012; 28 Wuxing (10.1016/j.ins.2022.07.087_b0225) 2004; 18 Solomon (10.1016/j.ins.2022.07.087_b0175) 1987; 35 Gee (10.1016/j.ins.2022.07.087_b0070) 2016 Tan (10.1016/j.ins.2022.07.087_b0205) 2007; 177 Van Veldhuizen (10.1016/j.ins.2022.07.087_b0210) 1998 |
| References_xml | – year: 2016 ident: b0070 article-title: Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands – year: 1988 ident: b0025 article-title: Radial basis functions, multi-variable functional interpolation and adaptive networks publication-title: Royal Signals – volume: 38 start-page: 9 year: 2000 end-page: 40 ident: b0105 article-title: Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning publication-title: Mach. Learn. – volume: 35 start-page: 254 year: 1987 end-page: 265 ident: b0175 article-title: Algorithms for the vehicle routing and scheduling problems with time window constraints publication-title: Oper. Res. – reference: K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002) 182–197. – reference: C. Liong, I. Wan, O. Khairuddin, Vehicle Routing Problem: Models and Solutions, J. Quality Meas. Anal. (2008). – reference: K. Tan, L. Lee, K. Ou, Artificial intelligence heuristics in solving vehicle routing problems with time window constraints, Eng. Appl. Artif. Intell. 14 (2001) 825–837. – volume: 18 start-page: 381 year: 2004 end-page: 389 ident: b0225 article-title: Classification of gear faults using cumulants and the radial basis function network publication-title: Mech. Syst. Signal Process. – reference: E. Berhan, B. Beshah, D. Kitaw, A. Abraham, Stochastic Vehicle Routing Problem: A Literature Survey, J. Inf. Knowl. Manage. 13 (2014). – reference: A. Hassanat, V. Prasath, M. Abbadi, S. Abu-Qdari, H. Faris, An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques, Information 9 (2018). URL: – start-page: 221 year: 1998 end-page: 228 ident: b0210 article-title: Evolutionary computation and convergence to a pareto front publication-title: Late Breaking Papers At The Genetic Programming 1998 Conference – volume: 78 start-page: 1481 year: 1990 end-page: 1497 ident: b0145 article-title: Networks for approximation and learning publication-title: Proc. IEEE – reference: Y. Niu, D. Kong, R. Wen, Z. Cao, J. Xiao, An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand, Knowl.-Based Syst. 230 (2021) 107378. – reference: F. Schwenker, H. Kestler, G. Palm, Contributed article Three (2000). – volume: 1621 year: 1996 ident: b0095 article-title: Function approximation with neural networks and local methods: Bias, variance and smoothness publication-title: Australian Conference On Neural Networks – year: 2020 ident: b0100 article-title: The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution model publication-title: Soft. Comput. – reference: X. Zhang, Y. Tian, R. Cheng, Y. Jin, An Efficient Approach to Non-dominated Sorting for Evolutionary Multi-objective Optimization, IEEE Trans. Evol. Comput. 19 (2015) 201–213. – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: b0150 article-title: Induction of Decision Trees publication-title: Mach. Learn. – year: 1995 ident: b0195 article-title: Fault tolerant design using single and multicriteria genetic algorithm optimization publication-title: Massachusetts Institute of Technology – year: 1979 ident: b0040 article-title: The Vehicle Routing Problem publication-title: Traveling Salesman Problem – start-page: 1 year: 2015 end-page: 11 ident: b0130 article-title: Performance metrics in multi-objective optimization publication-title: 2015 Latin American Computing Conference (CLEI) – volume: 6 start-page: 163 year: 2005 end-page: 190 ident: b0045 article-title: Solving multiobjective optimization problems using an artificial immune system publication-title: Genetic Programming Evolvable Mach. – volume: 36 start-page: 185 year: 2014 end-page: 191 ident: b0055 article-title: Computational complexity measures for many-objective optimization problems publication-title: Proc. Comput. Sci. – reference: . – volume: 177 year: 2007 ident: b0205 article-title: Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation publication-title: Eur. J. Oper. Res. – volume: 74 start-page: 2594 year: 2011 end-page: 2602 ident: b0075 article-title: Global and local modelling in RBF networks publication-title: Neurocomputing – year: 2007 ident: b0065 article-title: Meshfree approximation methods with MATLAB – reference: D. Bertsimas, A Vehicle Routing Problem with Stochastic Demand, Oper. Res. 40 (1992) 574–585. – volume: 236 start-page: 425 year: 2016 end-page: 461 ident: b0190 article-title: Variable neighborhood search for the stochastic and dynamic vehicle routing problem publication-title: Ann. Oper. Res. – volume: 28 start-page: 225 year: 2012 end-page: 236 ident: b0215 article-title: The LEM3 System for Multitype Evolutionary Optimization publication-title: Comput. Informatics – volume: 13 start-page: 469 year: 1971 end-page: 475 ident: b0015 article-title: Mean square error of prediction as a criterion for selecting variables publication-title: Technometrics – reference: J. Moody, C. Darken, Learning with localized receptive fields, Yale Univ., Department of Computer Science, 1988. – reference: S. Mirjalili, Genetic algorithm, Evol. Algorithms Neural Networks (2019) 43–55. – volume: 28 start-page: 225 year: 2012 ident: 10.1016/j.ins.2022.07.087_b0215 article-title: The LEM3 System for Multitype Evolutionary Optimization publication-title: Comput. Informatics – ident: 10.1016/j.ins.2022.07.087_b0080 doi: 10.3390/info9070167 – ident: 10.1016/j.ins.2022.07.087_b0050 doi: 10.1109/4235.996017 – start-page: 1 year: 2015 ident: 10.1016/j.ins.2022.07.087_b0130 article-title: Performance metrics in multi-objective optimization publication-title: 2015 Latin American Computing Conference (CLEI) – year: 2020 ident: 10.1016/j.ins.2022.07.087_b0100 article-title: The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution model publication-title: Soft. Comput. doi: 10.1007/s00500-019-04312-9 – year: 1979 ident: 10.1016/j.ins.2022.07.087_b0040 article-title: The Vehicle Routing Problem publication-title: Traveling Salesman Problem – ident: 10.1016/j.ins.2022.07.087_b0035 doi: 10.1287/opre.40.3.574 – volume: 38 start-page: 9 year: 2000 ident: 10.1016/j.ins.2022.07.087_b0105 article-title: Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning publication-title: Mach. Learn. doi: 10.1023/A:1007677805582 – volume: 1 start-page: 81 year: 1986 ident: 10.1016/j.ins.2022.07.087_b0150 article-title: Induction of Decision Trees publication-title: Mach. Learn. doi: 10.1023/A:1022643204877 – ident: 10.1016/j.ins.2022.07.087_b0030 doi: 10.1142/S0219649214500221 – volume: 236 start-page: 425 year: 2016 ident: 10.1016/j.ins.2022.07.087_b0190 article-title: Variable neighborhood search for the stochastic and dynamic vehicle routing problem publication-title: Ann. Oper. Res. doi: 10.1007/s10479-015-1949-7 – volume: 13 start-page: 469 year: 1971 ident: 10.1016/j.ins.2022.07.087_b0015 article-title: Mean square error of prediction as a criterion for selecting variables publication-title: Technometrics doi: 10.1080/00401706.1971.10488811 – volume: 35 start-page: 254 year: 1987 ident: 10.1016/j.ins.2022.07.087_b0175 article-title: Algorithms for the vehicle routing and scheduling problems with time window constraints publication-title: Oper. Res. doi: 10.1287/opre.35.2.254 – ident: 10.1016/j.ins.2022.07.087_b0135 doi: 10.1016/j.knosys.2021.107378 – ident: 10.1016/j.ins.2022.07.087_b0090 – year: 1988 ident: 10.1016/j.ins.2022.07.087_b0025 article-title: Radial basis functions, multi-variable functional interpolation and adaptive networks publication-title: Royal Signals – ident: 10.1016/j.ins.2022.07.087_b0125 doi: 10.1007/978-3-319-93025-1_4 – ident: 10.1016/j.ins.2022.07.087_b0245 doi: 10.1109/TEVC.2014.2308305 – ident: 10.1016/j.ins.2022.07.087_b0200 doi: 10.1016/S0952-1976(02)00011-8 – volume: 74 start-page: 2594 year: 2011 ident: 10.1016/j.ins.2022.07.087_b0075 article-title: Global and local modelling in RBF networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.03.027 – start-page: 221 year: 1998 ident: 10.1016/j.ins.2022.07.087_b0210 article-title: Evolutionary computation and convergence to a pareto front – volume: 36 start-page: 185 year: 2014 ident: 10.1016/j.ins.2022.07.087_b0055 article-title: Computational complexity measures for many-objective optimization problems publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2014.09.077 – year: 1995 ident: 10.1016/j.ins.2022.07.087_b0195 article-title: Fault tolerant design using single and multicriteria genetic algorithm optimization publication-title: Massachusetts Institute of Technology – year: 2007 ident: 10.1016/j.ins.2022.07.087_b0065 – volume: 78 start-page: 1481 year: 1990 ident: 10.1016/j.ins.2022.07.087_b0145 article-title: Networks for approximation and learning publication-title: Proc. IEEE doi: 10.1109/5.58326 – year: 2016 ident: 10.1016/j.ins.2022.07.087_b0070 – ident: 10.1016/j.ins.2022.07.087_b0120 – volume: 18 start-page: 381 year: 2004 ident: 10.1016/j.ins.2022.07.087_b0225 article-title: Classification of gear faults using cumulants and the radial basis function network publication-title: Mech. Syst. Signal Process. doi: 10.1016/S0888-3270(03)00080-3 – volume: 1621 year: 1996 ident: 10.1016/j.ins.2022.07.087_b0095 article-title: Function approximation with neural networks and local methods: Bias, variance and smoothness publication-title: Australian Conference On Neural Networks – volume: 177 year: 2007 ident: 10.1016/j.ins.2022.07.087_b0205 article-title: Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.12.029 – ident: 10.1016/j.ins.2022.07.087_b0185 – volume: 6 start-page: 163 issue: 2 year: 2005 ident: 10.1016/j.ins.2022.07.087_b0045 article-title: Solving multiobjective optimization problems using an artificial immune system publication-title: Genetic Programming Evolvable Mach. doi: 10.1007/s10710-005-6164-x |
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